Predicting Saturated Hydraulic Conductivity for Estimating Maximum Soil Infiltration Rates

Abstract

Pedotransfer functions (PTFs) can be used to estimate saturated hydraulic conductivity (Ks) from soil properties such as texture and bulk density. We evaluated several published PTFs to determine which was the most reliable for predicting maximum soil infiltration rates for soils in Dane County, Wisconsin. The PTFs were evaluated with a local database of measured infiltration rates (using a 5000‐cm2 infiltrometer), land use, soil properties (texture, bulk density, and organic matter content), and topographic properties (elevation, slope, and aspect) at 42 locations. We used Bayesian updating to combine local and national data to develop Dane County Ks estimates for some soil textural classes, some of which were subdivided into two porosity classes. We developed a local PTF for predicting Ksand examined the potential for using nonsoil properties (land use and topography) as predictors of Ks. The local PTF used the mass fraction of sand and bulk density as predictors; adding nonsoil predictors did not improve its accuracy. Although the local PTF had a lower root mean squared error (RMSE) than the published PTFs, it should be evaluated with an independent dataset. Of the published PTFs evaluated, the most reliable Ks estimate compared to the local database was the one used for soils with strong structure in the precision agricultural‐landscape modeling system (PALMS), a fine‐scale landscape process model. The Ks estimates developed by Bayesian updating corresponded well with those used in PALMS for soils with strong structure.